Choice probability generating functions
AbstractThis paper establishes that every random utility discrete choice model (RUM) has a representation that can be characterized by a choice-probability generating function (CPGF) with specific properties, and that every function with these specific properties is consistent with a RUM. The choice probabilities from the RUM are obtained from the gradient of the CPGF. Mixtures of RUM are characterized by logarithmic mixtures of their associated CPGF. The paper relates CPGF to multivariate extreme value distributions, and reviews and extends methods for constructing generating functions for applications. The choice probabilities of any ARUM may be approximated by a cross-nested logit model. The results for ARUM are extended to competing risk survival models.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 24214.
Date of creation: 2010
Date of revision:
Discrete choice; random utility; mixture models; duration models; logit; generalised extreme value; multivariate extreme value;
Other versions of this item:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-08-14 (All new papers)
- NEP-DCM-2010-08-14 (Discrete Choice Models)
- NEP-ECM-2010-08-14 (Econometrics)
- NEP-UPT-2010-08-14 (Utility Models & Prospect Theory)
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